Abstract
In recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogeny trees and inferring cancer progression. However, recent studies leveraging Single Cell Sequencing (SCS) techniques showed evidence of a number of recurrence and mutational loss in several tumor samples, an observation which essentially violates a strict ISA (e.g., J. Kuipers et al., Genome Research, 2017). We present the SASC (Simulated Annealing Single Cell inference) tool, a new model and a robust framework based on Simulated Annealing for the inference of cancer progression from the SCS data. Our main objective is to overcome the limitations of the Infinite Sites Assumption by introducing a version of the Dollo parsimony model which indeed allows the deletion of mutations from the evolutionary history of the tumor. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real data sets and in comparison with other available methods. The Simulated Annealing Single Cell inference tool (SASC) is open source and available at https://github.com/sciccolella/sasc.
